Transmission x-ray systems rely on the measured photon attenuation coefficients for material imaging and classification. While this approach provides high quality imaging capabilities and satisfactory object discrimination in most situations, it lacks material-specific information. For airport security, this can be a significant issue as false alarms require additional time to be resolved by human operators, which impacts bag throughput and airport operations. Orthogonal techniques such as X-ray Diffraction Tomography (XRDT) using a coded aperture provide complementary chemical/molecular signatures that can be used to identify a target material. The combination of noisy signals, variability in the XRD form factors for the same material, and the lack of a comprehensive material library limits the classification performance of the correlation based methods. Using simulated data to train a 1D Convolution Neural Network (CNN), we found relative improvements in classification accuracy compared to the correlation based approach we used previously. These improvement gains were cross-validated using the simulated data, and provided satisfactory detection results against real experimental data collected on a laboratory prototype.
KEYWORDS: Signal to noise ratio, Machine learning, Tomography, X-rays, X-ray diffraction, Data modeling, Coded apertures, Data processing, Explosives, Dimension reduction
The material-specific information contained in X-ray diffraction (XRD) measurements make it attractive for the detection of threats in airport baggage. Spatially-localized XRD signatures at each voxel in a bag may be obtained with a snapshot via coded aperture XRD tomography, but measurement unceratinty due to data processing and low SNR can lead to loss in information. We use machine learning and non-linear dimension reduction to identify threat and non-threat items in a way that overcomes these variations in the data. We observe the emergence of clusters from the data, possibly providing new prospects for XRD-based classification. We further show improved performance using machine learning methods relative to a conventional, correlation-based classifier in the low-SNR regime.
Simulations of x-ray scanners have the potential to aid in the design and understanding of system performance. We have previously shown the usefulness of a high-throughput simulation framework in pursuit of information theoretic analysis of x-ray systems employed for aviation security. While conclusions drawn from these studies were able to inform design decisions, they were limited to generic system geometries and na¨ıve interpretations of detector responses. In collaboration with the SureScan Corporation, we have since expanded our analysis efforts to include their real world system geometry and detector response. To this extent, we present our work to simulate the SureScan x1000 scanner, a fixed-gantry spectral CT system for checked baggage. Our simulations are validated in terms of system geometry and spectral response. We show how high fidelity simulations are used with SureScan reconstruction software to analyze virtual baggage. The close match between simulated and real world measurements means that simulation can be a powerful tool in system development. Moreover, the close match allows simulation to be a straightforward avenue for producing large labeled datasets needed in machine learning approaches to automatic threat recognition (ATR).
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